Overview

Notebook developed with R version 4.2.2

Use Case

  • Interoperable IFCB data product for the CA HAB Bulletin
  • IFCB Dashboard to OBIS use case with automated classification
  • Event core with Occurrence extension

Authors

Ian Brunjes (SCCOOS), Stace Beaulieu (WHOI) Prepared for OBIS IOOS Marine Biological Data Mobilization Workshop April 2023

This is a prototype for testing purposes only. A protocol is being developed to determine if and when appropriate to submit products from automated classification to OBIS.

Sponsored by NOAA PCMHAB20 project “Harmful Algal Bloom Community Technology Accelerator”

Workflow

Main steps in the workflow can be related to the IFCB workflow diagram in the ifcb-analysis wiki on GitHub

Step: Classification - Interpretation for the autoclass scores / transform automated classification into presence/absence

Step: Matching class labels to scientific names and IDs - Match the class labels to scientific names in the World Register of Marine Species (WoRMS) taxonomic database.

Step: Summarization - Calculate concentration as number of ROIs classified to a taxon divided by volume analyzed

Next step (not shown / would extend the diagram): Transforming to Darwin Core - Map resulting data table into Darwin Core table(s)

Target data product to standardize to Darwin Core:

Concentration of 2 genera of HAB taxa from an IFCB sample(s) (e.g., here is a sample with autoclass available in HABDAC at Del Mar Mooring

Preconditions: - IFCB Dashboard sample (bin) has autoclass csv file with scores per class label from automated classifier - IFCB Dashboard sample has been populated with a dataset name, volume_analyzed, datetime, latitude, longitude, and depth - For autoclass labels: A lookup table has been prepared with thresholds per class label

This workflow is being developed to meet the EU Horizon 2020 “Best practices and recommendations for plankton imagery data management” http://dx.doi.org/10.25607/OBP-1742

Classification

In this step, we interpret the autoclass scores from the autoclass.csv file on the IFCB Dashboard. We will filter to the targeted class labels, apply a threshold per class label, and determine the “winning” class label per ROI, thus transforming the automated classification into a presence/absence table.

target_labels = read_csv(here("data", "target_classification_labels.csv"))

kable(target_labels) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
label intended_worms_taxon autoclass_threshold
Alexandrium catenella Alexandrium 0.2
Pseudo-nitzschia Pseudo-nitzschia 0.7
pennate Pseudo-nitzschia Pseudo-nitzschia 0.7

The order of operations is important in this filtering and thresholding process. If the filtering is applied prior to thresholding, the concentration is possibly (likely) to be overestimated by excluding other classes that may have higher scores.

Thresholds used in this prototype are for testing purposes only.

Our initial prototype will retain as ‘absence’ (zero count) when no ROIs exceed per-class threshold. However, we acknowledge that data providers may want to use a different per-class threshold to report absence, and might only want to report presence.

Once the output from this Classification step is matched to scientific names and IDs (next step) the intermediate table loosely corresponds to Level 1b SeaBASS file (classification per ROI).

sample_bin = "D20210926T181303_IFCB158"
bin_details = get_bin_details(sample_bin)
# Thresholds used in this prototype are for testing purposes only.

bin_occurrences = get_bin_occurrences(sample_bin, target_labels)
kable(bin_occurrences) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
pid class score
D20210926T181303_IFCB158_01203 Pseudo-nitzschia 0.9565
D20210926T181303_IFCB158_01634 Pseudo-nitzschia 0.7026
D20210926T181303_IFCB158_01650 Pseudo-nitzschia 0.9560

Matching class labels to scientific names and IDs

In this step, we use (a portion of) each autoclass label to query the API of the World Register of Marine Species (WoRMS) taxonomic database to return an accepted scientific name, its paired Aphia ID, and its taxon rank and kingdom.

wm_records = get_worms_taxonomy(target_labels$intended_worms_taxon)
kable(wm_records) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
AphiaID scientificname lsid rank kingdom intended_worms_taxon
109470 Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista Alexandrium
149151 Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista Pseudo-nitzschia

Summarization

In this step, we use the table from the Classification step and results from the Matching to WoRMS step to calculate concentration as number of ROIs classified to a taxon divided by volume analyzed per sample.

Output from the Summarization step loosely corresponds to Level 2 SeaBASS file.

occurrences_summary = summarize_bin_occurrences(bin_details, bin_occurrences, target_labels)
kable(occurrences_summary) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
intended_worms_taxon occurrences taxon_classes associated_rois occurrences_per_ml sampleTime lat lng bin_id
Alexandrium 0 Alexandrium catenella 0.0000000 2021-09-26T18:13:03+00:00 32.92917 -117.3165 D20210926T181303_IFCB158
Pseudo-nitzschia 3 Pseudo-nitzschia | pennate Pseudo-nitzschia _01203 | _01634 | _01650 0.7109005 2021-09-26T18:13:03+00:00 32.92917 -117.3165 D20210926T181303_IFCB158

Transforming to Darwin Core

In this step, we transform the table from the Summarization step into two tables (an event table and an occurrence table) and add columns to meet OBIS and GBIF requirements for the Darwin Core Archive package.

We also add columns to meet the EU Horizon 2020 “Best practices and recommendations for plankton imagery data management” http://dx.doi.org/10.25607/OBP-1742

To assign the unique occurrenceID for the concentrations per taxon per sample we used eventID_taxonID, a pattern similar to the EU best practice, such that an individual included in the summed count would be represented by eventID_taxonID_roiID. Ultimately, we would like to test the DwC ResourceRelationship extension and/or DwC term associatedOrganisms to relate individuals to abundances reported to OBIS.

# Build the Darwin Core Event table
event_tbl = build_event_table(bin_details)
kable(event_tbl) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
datasetName eventID eventDate decimalLongitude decimalLatitude countryCode geodeticDatum minimumDepthInMeters maximumDepthInMeters sampleSizeValue sampleSizeUnit
del-mar-mooring D20210926T181303_IFCB158 2021-09-26T18:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.22 milliliter
# Join occurrence summary with taxon records
wm_occurrences = left_join(occurrences_summary, wm_records, by = c("intended_worms_taxon" = "intended_worms_taxon"))

# Build the Darwin Core Occurrence table
occurrence_tbl = build_occurrence_table(wm_occurrences, bin_details)
kable(occurrence_tbl) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%")
eventID occurrenceID basisOfRecord identifiedBy identificationVerificationStatus identificationReferences identificationRemarks associatedMedia verbatimIdentification scientificName scientificNameID taxonRank kingdom occurrenceStatus organismQuantity organismQuantityType institutionCode
D20210926T181303_IFCB158 D20210926T181303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T181303_IFCB158 D20210926T181303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01203 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01634 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01650 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.7109005 counts per milliliter AxiomROR

Extending to multiple events for time period

# Demonstrating how to build the DwC tables for all bins within a span of time
start_date = "2021-09-25"
end_date = "2021-09-27"

# Read in classification input parameters
target_labels = read_csv(here("data", "target_classification_labels.csv"))

# Query WORMS lookup table
wm_records = get_worms_taxonomy(target_labels$intended_worms_taxon)

# Get the ifcb bins within time span and construct
bin_ids = get_bins_in_range(start_date, end_date)

event_tables = list()
occurrence_tables = list()

for(bin in bin_ids) {
  if (bin_has_autoclass(bin)) {
    # Build occurrence summary
    bin_details = get_bin_details(bin)
    bin_occurrences = get_bin_occurrences(bin, target_labels)
    occurrences_summary = summarize_bin_occurrences(bin_details, bin_occurrences, target_labels)
    
    # Build dwc event table
    event_tbl = build_event_table(bin_details)
    
    # Build dwc occurrence table
    wm_occurrences = left_join(occurrences_summary, wm_records, by = c("intended_worms_taxon" = "intended_worms_taxon"))
    occurrence_tbl = build_occurrence_table(wm_occurrences, bin_details)
    
    event_tables[bin] = list(event_tbl)
    occurrence_tables[bin] = list(occurrence_tbl)
  }
}

# Bind each per bin result into single event/occurrence table
event_tbl = bind_rows(event_tables)
occurrence_tbl = bind_rows(occurrence_tables)

# Save to output to .csv
write_csv(event_tbl, here("output", "event.csv"))
write_csv(occurrence_tbl, here("output", "occurrence.csv"))

Aggregated event table

kable(event_tbl) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%", height = "400px")
datasetName eventID eventDate decimalLongitude decimalLatitude countryCode geodeticDatum minimumDepthInMeters maximumDepthInMeters sampleSizeValue sampleSizeUnit
del-mar-mooring D20210926T211303_IFCB158 2021-09-26T21:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.336 milliliter
del-mar-mooring D20210926T181303_IFCB158 2021-09-26T18:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.220 milliliter
del-mar-mooring D20210926T151303_IFCB158 2021-09-26T15:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.482 milliliter
del-mar-mooring D20210926T121304_IFCB158 2021-09-26T12:13:04+00:00 -117.3165 32.92917 US WGS84 0 0 4.538 milliliter
del-mar-mooring D20210926T091303_IFCB158 2021-09-26T09:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.578 milliliter
del-mar-mooring D20210926T061303_IFCB158 2021-09-26T06:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.616 milliliter
del-mar-mooring D20210926T031304_IFCB158 2021-09-26T03:13:04+00:00 -117.3165 32.92917 US WGS84 0 0 4.562 milliliter
del-mar-mooring D20210926T001304_IFCB158 2021-09-26T00:13:04+00:00 -117.3165 32.92917 US WGS84 0 0 4.581 milliliter
del-mar-mooring D20210925T211305_IFCB158 2021-09-25T21:13:05+00:00 -117.3165 32.92917 US WGS84 0 0 4.328 milliliter
del-mar-mooring D20210925T181304_IFCB158 2021-09-25T18:13:04+00:00 -117.3165 32.92917 US WGS84 0 0 4.289 milliliter
del-mar-mooring D20210925T151304_IFCB158 2021-09-25T15:13:04+00:00 -117.3165 32.92917 US WGS84 0 0 4.166 milliliter
del-mar-mooring D20210925T121306_IFCB158 2021-09-25T12:13:06+00:00 -117.3165 32.92917 US WGS84 0 0 4.381 milliliter
del-mar-mooring D20210925T091303_IFCB158 2021-09-25T09:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.554 milliliter
del-mar-mooring D20210925T061303_IFCB158 2021-09-25T06:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.580 milliliter
del-mar-mooring D20210925T031303_IFCB158 2021-09-25T03:13:03+00:00 -117.3165 32.92917 US WGS84 0 0 4.558 milliliter
del-mar-mooring D20210925T001302_IFCB158 2021-09-25T00:13:02+00:00 -117.3165 32.92917 US WGS84 0 0 4.436 milliliter

Aggregated occurrence table

kable(occurrence_tbl) %>% kable_material(c("striped", "hover")) %>% scroll_box(width = "100%", height = "400px")
eventID occurrenceID basisOfRecord identifiedBy identificationVerificationStatus identificationReferences identificationRemarks associatedMedia verbatimIdentification scientificName scientificNameID taxonRank kingdom occurrenceStatus organismQuantity organismQuantityType institutionCode
D20210926T211303_IFCB158 D20210926T211303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T211303_IFCB158 D20210926T211303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T211303_IFCB158&image=00672 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T211303_IFCB158&image=00950 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.4612546 counts per milliliter AxiomROR
D20210926T181303_IFCB158 D20210926T181303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T181303_IFCB158 D20210926T181303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01203 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01634 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T181303_IFCB158&image=01650 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.7109005 counts per milliliter AxiomROR
D20210926T151303_IFCB158 D20210926T151303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T151303_IFCB158 D20210926T151303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T151303_IFCB158&image=00999 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.2231147 counts per milliliter AxiomROR
D20210926T121304_IFCB158 D20210926T121304_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T121304_IFCB158&image=00076 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T121304_IFCB158&image=00928 Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista present 0.4407228 counts per milliliter AxiomROR
D20210926T121304_IFCB158 D20210926T121304_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T121304_IFCB158&image=00739 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T121304_IFCB158&image=01033 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.4407228 counts per milliliter AxiomROR
D20210926T091303_IFCB158 D20210926T091303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T091303_IFCB158 D20210926T091303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00133 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00188 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00519 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00556 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00575 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T091303_IFCB158&image=00673 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 1.3106160 counts per milliliter AxiomROR
D20210926T061303_IFCB158 D20210926T061303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T061303_IFCB158 D20210926T061303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T061303_IFCB158&image=00481 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T061303_IFCB158&image=00882 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.4332756 counts per milliliter AxiomROR
D20210926T031304_IFCB158 D20210926T031304_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T031304_IFCB158 D20210926T031304_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T031304_IFCB158&image=00188 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T031304_IFCB158&image=00982 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.4384042 counts per milliliter AxiomROR
D20210926T001304_IFCB158 D20210926T001304_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210926T001304_IFCB158 D20210926T001304_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210926T001304_IFCB158&image=00694 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.2182929 counts per milliliter AxiomROR
D20210925T211305_IFCB158 D20210925T211305_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T211305_IFCB158 D20210925T211305_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00078 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00110 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00164 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00287 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00563 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00717 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00749 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00845 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=00946 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01037 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01056 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01220 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01222 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01256 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01620 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T211305_IFCB158&image=01688 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 3.6968577 counts per milliliter AxiomROR
D20210925T181304_IFCB158 D20210925T181304_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T181304_IFCB158 D20210925T181304_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=00186 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=00227 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=00675 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=00676 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=01000 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=01416 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=01465 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=01728 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T181304_IFCB158&image=01824 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 2.0983912 counts per milliliter AxiomROR
D20210925T151304_IFCB158 D20210925T151304_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=01191 Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista present 0.2400384 counts per milliliter AxiomROR
D20210925T151304_IFCB158 D20210925T151304_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00125 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00272 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00364 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00438 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00606 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00892 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00905 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00908 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=00952 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=01016 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=01201 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=01358 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T151304_IFCB158&image=01959 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 3.1204993 counts per milliliter AxiomROR
D20210925T121306_IFCB158 D20210925T121306_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T121306_IFCB158 D20210925T121306_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=00364 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=00441 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=00690 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=00943 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=01005 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=01117 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T121306_IFCB158&image=01352 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 1.5978087 counts per milliliter AxiomROR
D20210925T091303_IFCB158 D20210925T091303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T091303_IFCB158 D20210925T091303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T091303_IFCB158&image=00138 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T091303_IFCB158&image=00197 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T091303_IFCB158&image=00365 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.6587615 counts per milliliter AxiomROR
D20210925T061303_IFCB158 D20210925T061303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T061303_IFCB158 D20210925T061303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T061303_IFCB158&image=01003 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.2183406 counts per milliliter AxiomROR
D20210925T031303_IFCB158 D20210925T031303_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T031303_IFCB158 D20210925T031303_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T031303_IFCB158&image=00190 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T031303_IFCB158&image=00673 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T031303_IFCB158&image=01017 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T031303_IFCB158&image=01019 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.8775779 counts per milliliter AxiomROR
D20210925T001302_IFCB158 D20210925T001302_IFCB158_109470 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. Alexandrium catenella Alexandrium urn:lsid:marinespecies.org:taxname:109470 Genus Chromista absent 0.0000000 counts per milliliter AxiomROR
D20210925T001302_IFCB158 D20210925T001302_IFCB158_149151 MachineObservation PredictedByMachine Trained machine learning model: 20220416_Delmar_NES_1.ptl (recommend publishing to a community or institutional repository for DOI) | Software to run the trained machine learning model: https://github.com/WHOIGit/ifcb_classifier (recommend referring to GitHub release or commit if not published for DOI) | Software to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/ifcb_autoclass_eval.R | Input parameters to interpret autoclass scores: https://github.com/sccoos/OBIS_workshop_2023_IFCB/blob/15e4d3120e56f5fa7f3d1022c3f63f3281269042/data/target_classification_labels.csv Arbitrary threshold used for both presence and absence without testing for false positives. https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T001302_IFCB158&image=00368 | https://ifcb.caloos.org/image?dataset=del-mar-mooring&bin=D20210925T001302_IFCB158&image=01219 Pseudo-nitzschia | pennate Pseudo-nitzschia Pseudo-nitzschia urn:lsid:marinespecies.org:taxname:149151 Genus Chromista present 0.4508566 counts per milliliter AxiomROR

Visualizing results

# Join event timestamp to occurrences
eo = left_join(occurrence_tbl, select(event_tbl, eventID, eventDate), by = join_by(eventID)) %>% mutate(eventDate = as_datetime(eventDate))


# Built plotly
p = eo %>% ggplot(aes(x=eventDate, y=organismQuantity, color = scientificName)) + geom_point() + geom_line() + labs(x = "", y = "organismQuantity (counts per milliliter)", title = paste0("Del Mar IFCB Occurrence Data (", start_date, " - ", end_date, ")"))

ggplotly(p) %>% layout(legend = list(title = "", x = 0.75, y = 0.9))